In order to solve the problem that the fuzziness and uncertainty of real data may seriously affect the classification results of few-shot learning, a Fuzzy Prototype Network (FPN) based on fuzzy reasoning was proposed by improving and optimizing the traditional few-shot learning prototype network. Firstly, the image feature information was obtained from Convolutional Neural Network (CNN) and fuzzy neural network, respectively. Then, linear knowledge fusion was performed on the two obtained parts of information to obtain the final image features. Finally, to achieve the final classification effect, the Euclidean distance between each category prototype and the query set was measured. A series of experiments were carried out on the mainstream datasets Omniglot and miniImageNet for few-shot learning classification. On miniImageNet dataset, the model achieves accuracy of 49.38% under the experimental setting of 5-way 1-shot, accuracy of 67.84% under the experimental setting of 5-way 5-shot, and accuracy of 51.40% under the experimental setting of 30-way 1-shot; and compared with the traditional prototype network, the model also has the accuracy greatly improved on Omniglot dataset.

Coupled data fusion algorithms mainly improve estimation accuracy and explain related latent variables of the other coupled data sets by using the information of one data set. Aiming at the large number of coupled images existing in reality, based on the Coupled Matrix and Tensor Factorization-OPTimization (CMTF-OPT) algorithm in coupled data fusion, a Coupled Images Factorization-OPTimization (CIF-OPT) algorithm for coupled images was proposed. The corresponding theoretical analysis and experimental results show that the effects of coupled image fusion by CIF-OPT algorithm under different noise influence are robust, and better than those by other coupling algorithms. Particularly, the CIF-OPT algorithm can accurately restore an image of missing some data elements with its coupled image at a certain probability.

Since the efficiency of retrieval and query of mass and high-speed space targets remains in a low level, a construction method of sphere grid index to the space targets based on the orbit restraint was proposed. The advantage that the orbit of space target is relatively stable in earth inertial coordinate system was used in the method to achieve the stabilized index to high-speed moving objects by maintaining the list of the space targets that pass through the sphere subdivision grid. On this basis, a region query application scheme was proposed. Firstly, the query time period was dispersed according to a particular step value. Secondly, the boundary coordinates of the query region in the inertial space were calculated and the staggered mesh was confirmed. Then the space targets in the grid were extracted and the spatial relationship between targets and the region was calculated and estimated. Finally, the whole time period was queried recursively and the space targets transit query analysis was accomplished. In the simulation experiment, the consumed time of the traditional method by calculating one by one has a linear positive correlation with the target number, but it has no relevance with the region size. One target costs 0.09 ms on average. By contrast, the time of the proposed method in the paper shows a linear decrease with the decline of area size. When the number of the region grids is less than 2750, the time efficiency is higher than that of the comparison method. Furthermore, it can maintain a fairly good accuracy. The experimental results show that the proposed method can improve the efficiency of the query in the actual region application effectively.